GWAS Strategy
GWAS was run using MLM model in GCTA1.93.2. Note that I tried different strategies to directly fit covariates or pre-adjust phenotypes by the covariates. The beta correlation between different strategies will be shown as below.
Take SRS_RMB_sum in Probands (with FSIQ included as covariate) for example, I tried:
- Strategy 1: Phenotype pre-adjusted by age, sex, chip, FSIQ
- Strategy 2: Phenotype pre-adjusted by age, sex, chip, FSIQ and 20 PCs
- Strategy 3: directly fit age, sex, chip, FSIQ
- Strategy 4: directly fit age, sex, chip, FSIQ and 20 PCs
#grid.raster(readPNG("figures/beta_strategy.png")
grid.raster(readPNG("figures/beta_strategy.png"))
Note that considering the GWAS sample size, computational time and false positive rates, we will report the results below:
- based on Strategy2 for the same phenotype
- based on sum measurement for the same phenotype
Probands
All Individuals
- GWAS was run on all individuals including diverse ancestry backgrounds.
- Signals with association p-value < 1e-5 will be shown.
Association Summary
Fitting FSIQ
datatable(iqs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Not fitting FSIQ
datatable(noiqs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Manhattan Plot
Primary Variable
Fitting FSIQ
#grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_primary_withPCs.png")
img <- readPNG("figures/manhplot_probands_adjIQ_1e-5_primary_withPCs.png")
grid.raster(img)

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_primary_withPCs.png"))

Secondary Variable
Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_secondary_withPCs.png"))

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_secondary_withPCs.png"))

Europeans Only
- 6861972 QCd SNPs with MAF > 0.01 included
- 1946 European individuals are included
- Signals with association p-value < 1e-5 will be shown.
Association Summary
Fitting FSIQ
datatable(iqs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Not fitting FSIQ
datatable(noiqs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Manhattan Plot
Primary Variable
Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_primary_EUR_withPCs.png"))

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_primary_EUR_withPCs.png"))

Secondary Variable
Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_secondary_EUR_withPCs.png"))

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_secondary_EUR_withPCs.png"))

Probands & Unaffected Siblings
- Phenotypes for Probands and Unaffected Siblings are separately pre-adjusted by covariates and then RINT.
- Based on the phenotype distribution, we combine Probands and Unaffected Siblings.
- We run GWAS on combined data.
All Individuals
- GWAS was run on all individuals including diverse ancestry backgrounds.
- Signals with association p-value < 1e-5 will be shown.
Association Summary
Fitting FSIQ
datatable(iqs_probSibs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Not fitting FSIQ
datatable(noiqs_probSibs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Manhattan Plot
Primary Variable
Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_primary_withPCs.png"))

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_primary_withPCs.png"))

Secondary Variable
Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_secondary_withPCs.png"))

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_secondary_withPCs.png"))

Europeans Only
- GWAS was run on European individuals with N=3544.
- Signals with association p-value < 1e-5 will be shown.
Association Summary
Fitting FSIQ
datatable(iqs_probSibs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Not fitting FSIQ
datatable(noiqs_probSibs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Manhattan Plot
Primary Variable
Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_primary_EUR_withPCs.png"))

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_primary_EUR_withPCs.png"))

Secondary Variable
Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_secondary_EUR_withPCs.png"))

Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_secondary_EUR_withPCs.png"))
